Machine Learning Application To Predictive Energy Management

ABSTRACT

A system for automatically learning and adapting to the energy usage of an equipment operating according to a control input including at least one sensor for measuring an energy usage of the equipment an generating a baseline energy usage over time signature that is used to compare active energy usage measurements to so as to determine operational deviations. The system includes software that matches and compares equipment operation to established norms and can modify the functioning of the equipment when threshold deviations are detected. The system includes the ability to learn the functioning of the equipment and can adjust for dynamically changing conditions to avoid generation of false alerts or alarms while at the same time detecting longer term deviations that if left unchecked, could shorten the lifespan of the equipment and increase the costs associated with running the equipment.

FIELD OF THE INVENTION

The present invention relates to systems and methods using machinelearning for monitoring, learning and adjusting the function of energyconsuming devices. A model baseline may be established from monitoringpower consumption data over a period to create a unique signature forthe particular equipment, and changes to this signature are monitored todetect a deviation from an expected power consumption signature. Thesystem thus allows for the automatic triggering of signals that alter oradjust device operation, the issuance of remote commands, or thetriggering of service calls.

BACKGROUND OF THE INVENTION

Equipment that utilize electricity are designed with expected electricalusage parameters. For example, an engineer designing an air handler cancalculate an expected energy usage for the piece of equipment based uponthe size of the motor that is used and other criteria. This data is usedby contractors to size the electrical wiring that is used to providepower to the equipment to ensure safe and proper operation.

In addition to having an expected instantaneous power consumption,equipment can have a characteristic power consumption pattern thatcorresponds or is based on the particular installation and configurationof the facility where the equipment is installed. When a piece ofequipment operates, a characteristic power consumption can be identifiedby monitoring the electrical usage of the particular piece of equipmentover time to establish a historical usage pattern. For example, athermostat may function to turn on an HVAC system at a set point value.The power consumption of the HVAC equipment can be measured as adistinct spike in energy usage. After start up, the compressor and airhandler may stay on for a duration until the temperature reaches the setpoint, and once reached, HVAC equipment is cycled off. A commensuratedrop in energy consumption of the HVAC equipment can be measured. TheHVAC equipment will remain off until the room heats or cools to a valueat which time a heating or cooling cycle is triggered to start the HVACequipment again.

Consistent patterns over time can be monitored and analyzed to determineif a system installed in a facility is operating within normal expectedlimits based on previously measured power consumption patterns.Monitoring can comprise measurement of: voltage, current and/or powerfactor. Monitoring can also consist of looking at external sensors andtracking commands and diagnostic data depending on the capabilities ofthe equipment or the installation.

It may be difficult, however, to establish a reliable power consumptionpattern due to external variables that can significantly impact theconsistency of expected patterns. These include, for example, changes totemperature, humidity and airflow, which can all significantly impactenergy usage of installed equipment. Other variables, such as the numberof room occupants, direct sunlight through windows or external openingscan also impact expected energy usage patterns. It is contemplatedhowever, the each of these variables could be monitored and measured andsome reasonable range of variance could be factored into the establishedpatterns.

It is further contemplated that as equipment ages, both theinstantaneous energy usage for the equipment and the energy usagepatterns for the equipment in the facility, could change. It could bepossible to calculate these expected changes to adjust an expected usagepattern against which a piece of equipment is compared over the lifespan of the equipment. If the pattern or signature changes drasticallyor beyond a pre-set tolerance or threshold level, this could indicate apotential problem may exist. This identification may in turn, be used totrigger an automated action(s). For example, it is conceived that if anobserved pattern degrades beyond a preset boundary, an alert may begenerated and automated adjustments to the equipment may be affected. Inother instances the alert may result in a closer look at the system,such as taking more frequent measurements or the correlation of otherdata or potentially related measurements, such as diagnosticinformation, or data from other sensors for example.

To further highlight the cyclic nature of energy consumption patternsfor installed systems, consider as one example a deep freezer used in arestaurant environment. The freezer may be set for a set point of −20°F. When the internal temperature rises to −18° F., the compressoractivates and starts to cool the freezer until the −20° F. set point isachieved after which the compressor shuts off. This cycle of cooling andwarming creates a pattern that will repeat consistently with theduration of each interval being substantially identical withinreasonable tolerances. These cycles can be seen and measured with theelectrical power or current drawn by the compressor of the unit suchthat one would expect to see a spike in power consumption as theequipment turns on, and a settling into a normalized utilization levelover a period of time the compressor remains on to cool the freezer bythe required two degrees. Following this, it would be expected to see adrop in power utilization when the temperature set point is reached andthe compressor cycles off. It would be expected that the equipment wouldremain off until the freezer gradually warmed up to the preset −18° F.,at that point the cycle would repeat. Each of the cycles in the aboveexample, which can also be seen in FIG. 1, exhibits a consistentpattern, It is contemplated that these patterns could be monitored andacted upon to trigger automated actions if there is a noticeable changeor deviation from the expected pattern.

In any normal operating environment, additional variables come into playthat will impact the cycle described above. These variables couldinclude, opening the door of the freezer (the longer the duration andlarger the impact), introduction of warm items into the freezer (thegreater the number of items the larger the impact) and externaltemperature variances (the larger the variance the larger the impact).Other normal periodic operational functions could also impact theseexpected cycles such as defrost cycles and fan operation. While thesefactors may vary an expected pattern, they can be detected and factoredinto the monitoring system because, in most cases, these are onlytemporary deviations. For example, warm items eventually cool down, theair entering the freezer while the door is open eventually drops intemperature, and the room HVAC typically brings the room to a stableoperating temperature. In contrast, variances caused by equipment faultsor malfunctions that require intervention will persist as prolongedvariances over multiple cycles.

While the figures and examples provided in this application often depictpatterns as consisting of a single or a small number of cycles, systemscan learn longer and more complex cycles. For example, if the defrostcycle comes on once a week, this can be represented as a longermulti-event cycle. Variances in this case could include seeing thedefrost cycle come on more or less often. One reason to include theselonger and/or more complex cycles in a pattern, which is used forcomparison purposes, is to also diagnose the function these intermittentfeatures provide. For example, if the device in question had a defrostcycle that was no longer activated, or activated more often, or evenleft on, an immediate diagnosis can be made and appropriate actionstaken. This in turn will lead to quicker response to increased energyconsumption, reduce the cycling or operation of the equipment that wouldlead to premature equipment failure and prevent the spoilage ofperishables that may be contained in the space that is being serviced bythe equipment.

Some examples of abnormal variances could be caused by a reduction inthe level of coolant in the compressor, coils that have become dirty andtherefore are no longer effective, blocked vents that limit airflow, andlose or leaking seals in a unit's doors, All of these problems mayrequire some corrective action before the energy consumption will returnto its prior normal or expected pattern.

In addition to the expected performance of equipment that is installedin a particular location at a particular facility, individual equipmentregardless of installation is expected to function in a defined manner.While it is true that one motor of a particular size made by amanufacturer will have similar power consumption characteristics as asecond motor of the same size made by the same manufacturer, inpractice, each piece of equipment can have its own particular powerconsumption signature. Additionally, the variance from one piece ofequipment from another “identical” piece of equipment due tomanufacturing tolerances, can be mitigated by establishing a baselinewhen the particular equipment is first installed. This baseline could beused, for example, to measure the performance of a new compressor unit.If the new compressor unit does not meet an expected baseline withinexpected tolerances, the unit can be flagged as faulty and furtherinvestigation or possible replacement could be initiated.

In one example, a compressor may have a lifespan of a set number ofoperating hours. In a typical duty cycle, a gradual degradation ofefficiency and performance would be observed as the unit ages. Thisexpected degradation can be adjusted for in monitoring by adjusting anexpected baseline of operation or tolerance level over time so as toavoid false alarms being triggered for maintenance events. Additionally,it is conceivable that the end of life of the unit could be predictedbased on similar characteristics observed in similar units that havefailed prior to the unit in question. For example, with a large installbase of a given manufacturers units with an expected life span of a setnumber of operating hours, it could be predicted when a unit will failbased on an analysis of the measured energy consumption signatureproviding a basis for predictive analysis. This would be highlyadvantageous as failure of a unit could result in financial loss due tospoiling of inventory and even a potential dosing of a business forrepairs or replacement of the failed equipment. In some instances,replacement may require permits and outside service providers to performthe work at hand. Scheduling and performing such a replacement as ascheduled preventative maintenance activity would be preferable andcause less disruption and as such, an early warning indicator asdescribed herein would be highly beneficial.

One may also decide to proactively replace a unit early in its lifecycle due to new and emerging technologies. With the trend of risingelectricity costs, as older units degrade and the new technologyprovides significant energy savings, an inflection point could bereached where the savings in energy consumption over time could coverthe cost the new equipment or make an earlier replacement advantageous.The monitoring and measurement of the consumption patterns and expectedconsumption patterns could assist in determining the return oninvestment. The residual resale value of aging equipment could also befactored into this analysis.

Another problem associated with aging equipment is that of mechanicaldegradation or damage. Normal operation of the equipment causesdegradation of seals and gaskets around the door of a unit. Withoutmeasuring the energy consumption, it may be very difficult to assess theimpact of a leaking seal and how the compromised seal could besignificantly increasing energy consumption.

Still further, in commercial and industrial environments, heavyequipment such as forklifts or trollies may be used in moving suppliesin a facility. The operation of such equipment in the proximity of, forexample cooling units, could result in damage to the cooling equipmentor storage facility, For example, a door could be impacted and bent outof shape, a seal could begin to come loose, or a freezer could beimpacted and dented thereby damaging internal insulation or overallintegrity and seals. Monitoring could again detect any prolongeddeviance from an established operation pattern of the equipment thatemerges, which could show a degradation of the efficiency of theequipment.

Still another challenge is that commercial areas often comprise crampedspaces where supplies and packaging are sometimes stored in the samerooms as various equipment. Areas that require airflow may becomeobstructed such that the efficiency of the equipment suffers.

The result of these various different situations is that the equipment(e.g., the freezer unit) will not operate efficiently, and theelectricity consumption will not adhere to the expected pattern. Thesechanges in patterns should be detectable and intelligent data can beextracted from the observed deviation in patterns.

In addition to potential increased energy usage, in many scenariosdescribed above there are important health and safety factors toconsider. For example, refrigerated or frozen foods can spoil, blockedair vents could present a fire hazard and leaks could result in slipperysurfaces or electrical hazards and in some cases even toxic fumes.

While many of the examples above had been made in connection withcooling equipment for a freezer, similar factors can be consider whenapplied to various different types of equipment including an HVACsystem, a heater, a water heater of other appliances.

SUMMARY OF THE INVENTION

What is desired then is a system and method that can monitor energyutilization of specific equipment to compare that energy utilization toa baseline expected energy utilization.

It is also desired to provide a system and method that can create anexpected energy utilization for a particular piece of equipment based ona measured energy usage signature of the equipment.

It is further desired to provide a system and method that will monitorenergy utilization of specific equipment, which is compared to abaseline expected energy utilization that factors in multiple variablesto make automated decisions on maintenance, control, and service of theequipment being monitored.

It is further desired to provide a system and method that will diagnoseaberrations to the expected patterns and classify these into potentialequipment problems as well as use historical accuracy of thesepredictions to improve its classification system over time.

It is still further desired to provide a system and method that providesfor automatic monitoring and self-adjustment of the monitoring systemsuch that the monitoring system runs more efficiently and processes thereceived data in a manner that limits or eliminates generation of anyfalse alarms for non-typical but normal operation of the equipment.

In particular, the system and method provide for self-learning includingsoftware that monitors a variety of input data from equipment that isbeing measured and “learns” patterns of operation for the equipment. Thesystem specifically increases the operating efficiency of thecomputer-based monitoring system by allowing for dynamic adjustments inthe baseline operating parameters by considering dynamic inputs thatwould impact the operation of the equipment combined with expectedoperational data for the equipment taking into consideration the age,efficiency and life expectancy of the equipment. The result is a systemthat self-learns and self-adjusts resulting in much greater operatingefficiency through the accurate detection of any anomalies and thereduction in false positive alarms for both the computer-basedmonitoring system and the equipment that is being monitored.

The present invention is directed to systems and methods for monitoringand characterizing energy consuming devices, e.g., via a network, suchas a telecommunications network and/or the internet, e.g., using anelectronic device such as a mobile phone, tablet, computer, and thelike. The systems and methods herein may provide a baseline model forelectrical consumption of devices and a detection method when expectedusage characteristics do not adhere to conformity to the expected model.The automated issuance of remote-control commands, maintenance ordiagnostic commands including the triggering of service calls for thereplacement of equipment before failure or degradation is contemplated.

In some instances, the automated action may comprise taking additionaland different types of diagnostics measurements including running theequipment through a diagnostic sequence to gather more comprehensivedata. In other instances, the automated action may comprise adjustingthe running of the equipment to a preset level while or until thedetected deviation from the expected energy pattern can be resolved.Still further, the automated action could comprise running the equipmentthrough a sequence of steps that are modified based on the gathering ofmore comprehensive data from a measurement device or a set of relatedand potentially separate of independent sensors.

Additionally, a secondary device may be provided that includes acontroller, one or more sensors coupled to the controller configured todetect one or more in room parameters, e.g., occupancy, door openings,and the like and a wireless communication interface. The device may alsocommunicate through low power wireless signals, such as Wi-Fi or thelike, to a remote computer system, which may store data from thesensor(s), analyze the data, generate an action, and/or generate reportsand alerts based at least in part on the data.

In such an embodiment, a device is provided that includes a controller,one or more sensors coupled to the controller configured to detect thepower consumption of one or more devices that are being monitored whereeach includes a wireless communication interface. Power consumptioncould comprise a power measurement or a current measurement via one ormore current sensing devices (e.g., current transformers or the like).These devices may communicate through low power wireless signals, suchas Wi-Fi or comparable, to a remote computer system, which may storedata from the sensor(s), analyze the data, adjust the operation of theequipment, and/or generate reports and alerts based at least in part onthe data. This data may be used in conjunction with the other sensordata providing a framework within which a baseline for the equipment isset and monitoring the performance of the equipment at any given time.

In an exemplary embodiment, these sensors are placed in a facility tocapture temperature, humidity and room occupancy along with the powerconsumption of equipment, A single sensor for temperature and occupancymay be used depending on whether it is desired to measure temperatureand occupancy in multiple places in a facility. The sensor measuringpower consumption captures data at intervals sufficient to map abaseline. This would include turning the equipment on, measuring theequipment while it is running, and cycling the equipment off. It iscontemplated that multiple cycles should be measured. In oneconfiguration, non-sequential cycles may be measured taken at differenttimes of the day. In terms of data granularity, it is contemplated thatmeasurements should be in minutes or seconds. These measurements maycomprise a current measurement, a voltage measurement or a combinationof the two with time stamps for correlation for instantaneous power drawcalculation.

Taking an example device, such as a compressor used in refrigeration orHVAC to illustrate, readings may be taken over time to establishpatterns or a signature and then extrapolate a baseline of expectedusage. In an exemplary embodiment, these measurements are made onfactory equipment to establish an expected usage profile for theequipment when new. When equipment is installed, this baseline is usedto establish and measure variances from the factory installed equipmentthat is expected to function within a set of tolerances. If the baselineis not consistent with a factory-generated expected baseline for theequipment in question, then remedial action may be taken immediately toverify the installation, the equipment itself, or the monitoringequipment. The remedial action could include altering the function ofthe equipment and running a diagnostic to gather more comprehensivedata. As adjustments are made, or if the baseline of the installeddevice is within the expected tolerance, power consumption is thenmeasured over time and reported to a remote computer for storage andprocessing.

As the equipment runs according to its designed and set parameters,additional data derived from the additional sensors described above(door open, humidity, temperature etc.) may be combined with the powerusage data to create a dynamic baseline for which power consumption canbe fully analyzed to generate alerts, or take corrective action ifneeded. To illustrate, if a typical baseline measurement includes acycle of approximately 10 minutes to cool a freezer by 3 degrees to itsstep point value and the tolerances for reaching this goal is set to +2minutes, then any variance of more than 12 minutes could be flagged as atrigger for action. However, this may comprise a “normal” cycle in whichthe freezer slowly warms when no peripheral action is taken (e.g., thedoor has not been opened since the last cycle). Should a complementarysensor, such as a door open event be detected within a few minutes ofthe cycle, the tolerance could be adjusted. Likewise, the adjustmentcould correspond to the length of time the door was identified as open.Additionally, or alternatively, the system could disregard the nextcooling cycle entirely as opening the door may involve the addition offood items which will take an even longer duration to cool. Suchflexibility in programming illustrates how thresholds can be dynamicallyadjusted thereby allowing the system to avoid false positives.

In still another embodiment, the systems and methods may provide earlywarning indicators by confirming if the equipment maintains temperatureswithin a normal thermal efficiency range while all heating and coolingsystems remain functional. For example, if a properly sized unit is nolonger able to keep up with what should be a normal temperaturedifferential at times of peak temperature variations with the outsideenvironment. In such a case, the system may report when thresholds arenot met in the form of alarms or warnings, allowing for further analysisby supplying the captured test results and data for review. It may bethat changes to the space have resulted in the system being undersizedor improperly adjusted.

In still another configuration, an adjustment for the expected baselinemay allow for a gradual degradation of the equipment over time. Forexample, a device half way through an expected lifecycle may exhibitsome degradation in efficiency. This slope of degradation may becomesteeper during the remaining lifetime of the equipment. Thisdeterioration can be compensated for by applying known deteriorationvalues based on the age of the equipment, which will reduce falsepositives. It should be noted that even while the allowable values mayallow for, as an example 20% degradation after a set number ofoperational hours of service on a piece of equipment, these values maynot be used on their own as the degradation may also be measured fromprior readings including a measured rate of degradation for the specificequipment. A sudden degradation, even if values remain in the acceptablerange may still however, be flagged as an anomaly by the system.

For this application the following terms and definitions shall apply:

The term “data” as used herein means any indicia, signals, marks,symbols, domains, symbol sets, representations, and any other physicalform or forms representing information, whether permanent or temporary,whether visible, audible, acoustic, electric, magnetic, electromagneticor otherwise manifested. The term “data” as used to representpredetermined information in one physical form shall be deemed toencompass any and all representations of the same predeterminedinformation in a different physical form or forms.

The term “network” as used herein includes both networks andinternetworks of all kinds, including the Internet, and is not limitedto any particular network or inter-network.

The terms “coupled”, “coupled to”, “coupled with”, “connected”,“connected to”, and “connected with” as used herein each mean arelationship between or among two or more devices, apparatus, files,programs, applications, media, components, networks, systems,subsystems, and/or means, constituting any one or more of (a) aconnection, whether direct or through one or more other devices,apparatus, files, programs, applications, media, components, networks,systems, subsystems, or means, (b) a communications relationship,whether direct or through one or more other devices, apparatus, files,programs, applications, media, components, networks, systems,subsystems, or means, and/or (c) a functional relationship in which theoperation of any one or more devices, apparatus, files, programs,applications, media, components, networks, systems, subsystems, or meansdepends, in whole or in part, on the operation of any one or more othersthereof.

The terms “process” and “processing” as used herein each mean an actionor a series of actions including, for example, but not limited to, thecontinuous or non-continuous, synchronous or asynchronous, routing ofdata, modification of data, formatting and/or conversion of data,tagging or annotation of data, measurement, comparison and/or review ofdata, and may or may not comprise a program.

Once a piece of equipment is put into service, and the expected baselinehas been established, a monitoring exercise that accounts for the dutycycle and operational elements in the environment is created. Forexample, for a freezer at a fast food restaurant, the number of timesthe door is opened, the external temperature at the store, the hours ofoperation and occupancy which may affect both above items are measuredand becomes part of the calculation. These patterns both establish thebaseline for the equipment itself as well as the operations of thebusiness using the equipment.

The system is programmed to predict the outcomes and measurements suchthat variations outside of expected tolerances will generate alerts andpotentially cause automated actions to be taken, However, it iscontemplated that looking at longer term patterns will function toeliminate false positive alerts. This hysteresis of values can beadjusted depending on the severity of the situation and the potentialimpact of the anomaly.

For example, if the door is left open while loading the freezer, thiscould be accounted for by allowing for a value (say ½ hour) tocompensate. It would be expected that the freezer would return to anormal pattern of operation once the door is closed and the newlyintroduced food or items have cooled down. However, if the door hasremained open long enough that the freezer temperature and inside foodtemperatures rise to a point that the food may be spoiled, there is anurgent need to trigger alerts and actions. In another example, there maybe temperature sensitive medication or other matters that have a tightallowable temperature range that may require the establishment oftighter bounds or early and more urgent warnings or indicators.

Actions taken upon the reaching of established thresholds may not besimply binary. For example, an escalating level of urgency could beprovided where the initial alert may be an audible alarm on the doorwhich goes off reminding someone in close proximity to the freezer toclose the door. Failing this, additional escalation such as a contactsent to the front of the store, or a text message sent to a localmanager or duty staff. Escalation may go as far as dispatching the owneror a technician to the site or in some cases even remote-control action.In some cases, this may involve starting remote diagnostics, triggeringa reset of a piece of equipment or simply having someone look over awebcam at the piece of equipment for obvious signs of blockage. Even ifthe freezer cannot be repaired with a proper closing of the door, adispatch would allow for mitigating action with respect to in thecontents of the freezer.

Another example is a hot water heater. The normal cycle for hot watermeasured at the factory is similar to a freezer in that water enters thetank and is heated to a preset setting so that it is ready to use. Oncethe tank temperature is set, the heater element turns off. The hot waterremains in the insulated tank but gradually cools to another presetvalue that causes the heater to come back on again to reheat it to thepre-set and desired value. In such a case, a pattern of powerconsumption is measured as coming on with a spike to start the heater,staying on long enough to heat the water to the desired temperature, andthen going off for a period of time that the water cools to the setpoint that triggers the reheating of the water. Once in service, the hotwater tank will lose hot water as it is consumed while cold water isadded to the tank to refill it. This in turn functions to lower thetemperature of the water in the tank and starts the heating cycle again.The patterns of when hot water is used may follow a routine of washingdishes or cleaning up at certain periods of the day, which may beaccounted for in usage patterns.

One further example is one of an advanced refrigeration control thatdoes defrost cycle management. Such a unit is used to optimize defrostcycle timing, typically by reducing these based on data obtained fromsensors, such as frost detection. Occasionally, even such power savingequipment that works well to save power under normal circumstances, maycontribute to excessive consumption. For example, if a fan has ceasedfunctioning and the defrost cycle starts, assuming the fan is frozen inice, it's conceivable that the defrost cycle, a cycle that takes a lotof energy, may come on more often or even stay on repeatedly causingboth an increase in energy use as well as potentially other issues withthe equipment. In these situations, having sensors on multiple pieces ofequipment including the fan, and a temperature sensor inside the unit,would allow the system to override the defrost system until themalfunction could be addressed.

There are numerous examples where this could be implemented includingrefrigeration, hot water generation, freezers, ice makers, HVACequipment, ovens and toasters and so on. Sensors relating to temperatureand humidity could provide further information for the system to analyzeas well as air flow sensors and the like.

It is still further understood that certain equipment may have heavierusage at certain times of the day. For example, the business could bebaking bread and the ovens could be drawing a relatively large amount ofpower at certain times of the day. However, at other times the usage maybe much lighter. It is conceived that the system can learn the usageover time and generate usage patterns based on the business patterns.

While understanding the pattern deviations and establishing root causewith some probability is an important aspect of the invention, anotheraspect is the ability to automatically correlate the various sensors tobetter predict and then take automated actions. The establishment ofthis root cause table and confidence level and the ability of the systemto learn and adjust/expand this table are key elements. As thepredictions are proven true, confidence in the determination algorithmsincreases while when proven wrong, adjustments are made and algorithmsadjusted.

In one configuration, a system for automatically learning and adaptingto the energy usage of an equipment operating according to a controlinput, the system comprising: a computer having a storage and coupled toa network, a sensor coupled to the network and associated with theequipment, and software executing on the computer including an expectedenergy usage over time signature the equipment is expected to followduring operation for a time period. The system is provided such that thesensor measures the energy used by the equipment during operation for ameasured period of time, the sensor generating energy data based on ameasured energy usage for the measured period of time and transmittingthe energy data to the computer. The system is further provided suchthat the software generates an actual energy usage over time signaturebased on the received energy data for the measured period of time, anddeviation data is saved on the storage and includes a thresholddeviation from the expected energy usage over time signature. Finally,the system is provided such that the software compares the actual energyusage over time signature to the expected energy usage over timesignature, and wherein when the actual power usage signature exceeds thethreshold deviation, the software initiates an action selected from thegroup consisting of: running the equipment through a diagnostic routine,setting the equipment to a preset level of operation, setting theequipment to a preset duration of operation, turning the equipment off,cycling the equipment, generating an alarm and combinations thereof.

In another configuration, a system for automatically learning andadapting to the energy usage of an equipment operating according to acontrol input is provided comprising: a computer having a storage andcoupled to a network, a sensor coupled to the network and associatedwith the equipment where the sensor measures the energy used by theequipment during operation for a first time period, the sensorgenerating first time period energy data based on the measured energyusage during the first time period and transmitting the first timeperiod energy data to the computer. The system also includes softwareexecuting on the computer generating an energy usage over time baselinebased on the received first time period energy data, deviation datasaved on the storage comprising a threshold deviation from the energyusage over time baseline where the sensor measures the energy used bythe equipment during operation for a second time period, the sensorgenerating second time period energy data based on the measured energyusage during the second time period and transmitting the second timeperiod energy data to the computer. The system is provided such that thesoftware modifies the energy usage over time baseline based on thereceived second time period energy data to generate a modified energyusage over time baseline, the software modifies the deviation data basedon the received second time period energy data to generate a modifieddeviation data, and the software generates an expected energy usage overtime signature based on the modified energy usage over time baseline anda threshold deviation signature based on the modified deviation data.

Other aspects and features of the present invention will become apparentfrom consideration of the following description taken in conjunctionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate exemplary embodiments of the inventions, inwhich:

FIG. 1 is a drawing showing a typical consumption pattern for a cyclingelectrical device such as a refrigerator.

FIG. 2 shows variance patterns on the usage of a typical fridge based onexternal factors.

FIG. 3 shows the creation of a model based on the consumption patternsof a given device.

FIG. 4 shows a depiction of a matching pattern with HIGH accuracysuggesting equipment functioning normally.

FIG. 5 is a depictions of a LOW accuracy match suggesting equipmentproblems.

FIG. 6 is another depiction of a LOW accuracy match suggesting equipmentproblems.

FIG. 7 shows a flow diagram of the logic used to determine the model.

FIG. 8 shows a flow diagram of the logic used to compare a model withactual readings leading to automated actions and triggers.

FIG. 9 is a block diagram of an installation with sensors and meters tocapture the required data.

FIG. 10 is a block diagram of the system according to the invention.

FIG. 11 is a flow and decision diagram of the system according to FIG.10 illustrating the initiation cycle.

FIG. 12 is a flow and decision diagram of the system according to FIG.10 illustrating the functioning of the system monitoring the power usageof a piece of equipment.

DETAILED DESCRIPTION OF THE INVENTION

The reference numbers in specific figures refer to elements in thosefigures. Turning to the drawings, FIG. 1 shows a typical pattern ofelectrical power consumption of a refrigerator cycling its compressor tokeep its contents cool. The chart shows the power used (1) on theY-axis, and elapsed time (2) on the x-axis.

On the depicted graph starting on the x-axis time (2) we can see thatthere is little to no power consumption until a relatively large spike(3) of usage as the compressor cycles on. This peak is the compressorcoming on, and then quickly setting into a steady state consumption (4)level that remains active for a number of minutes. While the consumption(4) may have some variability, it is expected to be fairly consistentwhile the compressor is running. After the cycle of cooling, and whenthe refrigerator reaches a programmed set point, consumption (5) dropsas the compressor cycles off resulting in a drop in power usage. Thisstate with little to no usage (6) continues until the refrigerator warmsup to a point where the compressor cycles back on repeating the pattern(3′,4′,5′,6′). As can be seen in FIG. 1, this repeated pattern or cycle(7,7′) essentially repeats as the equipment associated with therefrigerator cycles on and off to maintain a set point.

Turning now to FIG. 2, similar patterns from FIG. 1 are shown but extendto show 4 cycles (14,14′,14″,14′″) obtained by monitoring and capturingdata points of the cycling of a refrigerator. When comparing the cycles(14,14′,14″,14′″) certain differences can be detected within the cyclein terms of the duration of the various identified aspects of the cycle.For example, the time between turning off the equipment and starting itagain shown in (10) and (11) may vary where the second cycle (11) isnoticeably longer. In such a case, when the equipment being monitored isa refrigerator, it indicates that the ideal temperature achieved whenthe refrigerator cooled to its desired set point was retained longer in(11) than it was in (10). In other words, the refrigerator warmed upfaster in cycle (14) than it did in cycle (14′). This may be due to anumber of reasons including, for instance, because a door was opened,warm food was placed in the fridge or some other event affected onlythis one cycle of cooling. Once the internal temperature of therefrigerator was again cooled to the set point, the duration of thecooling cycle again corresponded to the expected duration in (11). Thisvariance due to a common event is to be expected and would not warrantthe creation of an alarm. It can further be understood that peripheralsensors, such as a door sensor that will detect if a door has beenopened, can provide additional input to the system. The thresholddeviation signature that the system matches the measured power usageagainst can be adjusted based on whether the door is detected as openand for how long. Likewise, a temperature sensor in the refrigerator canprovide temperature data to the system that can also be used to adjustthe threshold deviation signature based on the actual measuredtemperature in the refrigerator.

Again, in FIG. 2, when looking at the duration that the refrigeratorstays on, it can be seen in (12) that the power to the compressor ismaintained longer that it is in (13). Again, this suggests that it takeslonger to cool the refrigerator to the set point, and the compressorstays on for a longer period. Since this pattern only affects one cycle,this deviation from “normal” operation can be classified as a transientevent such as an open door or the placement of warm food in therefrigerator causing a single cycle to work harder. Once the desiredtemperature is achieved and presumably the contents of the refrigeratorare cooled to the desired temperature, the normal duration of the cyclesresumes.

Turning now to FIG. 3, a measured pattern (20) for a given refrigeratorat a given location at a given point in time in its duty cycle isillustrated. From the readings in (20) a model (21) can be generatedthat comprises an expected consumption pattern for this particularrefrigerator equipment. For the depiction, only four cycles arecaptured, but this model can range from a single cycle to many cyclesspanning a longer period of time. Likewise, various cycles can bemonitored and measured at different times of the day in a non-sequentialmanner. The system seeks to identify repeating and consistent patternsto build the expected energy consumption signature for the particularequipment.

In FIG. 4 the same model is shown that was generated in FIG. 3, but nowwith an actual power usage measurement of a refrigerator (31)superimposed thereon. As can be seen there is some slight variance fromthe model to the curve shown in (33). This illustrates that while thematch is not identical, it is fairly close and can be tolerated withinset threshold.

In FIG. 5 the pattern in (42) is again matched up with another powerconsumption measurement (41). However, this this instance, it can beseen that the matching is highly inaccurate. FIG. 6 illustrates asimilar pattern.

The curves shown in FIG. 5 and FIG. 6 depict standard types ofdeviations from a “normal” power signature that indicates something withthe equipment is wrong and needs to be analyzed. In the case of FIG. 5,the cycles are all shorter than normal and this pattern is consistentlyrepeated over a long duration. For refrigeration equipment, this type ofpattern suggests that there is either a leak (failed seal or possibledamage) or an opening in the refrigerator (door not closing properly) asthe temperature set point is not being maintained for the properduration based on historical readings when the equipment was known to befunctioning properly. It could also suggest an issue with a sensor or amechanical or electrical problem. Regardless, this over-frequent cyclingof the compressor adds significant cost due to increased powerconsumption and the increased usage of the equipment will result in theequipment reaching end-of-life much sooner than expected due to theincreased usage.

FIG. 6 also shows a pattern whereby the compressor stays on for anextended period of time due to not being able to achieve the set pointtemperature within the expected time frame based on the expected powerusage signature or pattern. This particular signature when analyzed inconnection with refrigeration equipment, may suggest there is a problemwith ability of the equipment to efficiently cool the space caused by alow coolant level, or a potential problem with the compressor.Regardless, this cycling of the compressor will significantly increasethe power usage of the equipment.

While FIGS. 5 and 6 show examples of abnormal cycling of a compressor,these are not meant to be limiting as one of skill in the art canconceive of numerous other variants of how actual power usage deviatesfrom an expected power usage pattern or signature.

Turning now to FIG. 7 a flow diagram is depicted for capturing andestablishing a model from actual measurements. When the system ispowered up (50) power consumption data is captured via sensors or meterson the equipment (51). This data is stored over time (52) to establishand create plots as shown in the preceding figures (53).

Once it is determined that a sufficient number of measurements areobtained, a pattern is established (53) and the system determines anoptimal duration for this pattern (54). Further, a typical variance (55)is measured within pattern cycles to allow for the limiting or completeelimination of false positives. It should be noted that this patterncould also account for the effect of external variances. For example, asensor for room occupancy for an HVAC system may offset the expectedusage and add an additional amount of acceptable time for a cycle tocomplete, the knowledge that the delta between the inside temperatureand the outside temperature is relatively large can also be accountedfor as the expected utilization of HVAC equipment would be larger (e.g.,more use in hotter summer months). In the case of a refrigerator, theroom temperature could be considered. Many such influences can bemeasured and accounted for in the model depending on the equipment beingmonitored. This expected power usage pattern or signature and theallowable variance therefrom is captured mathematically (56) and is usedto generate alerts and alarms. It will be understood by those of skillin the art that a variance or deviation from the expected energy orpower usage pattern or signature can trigger an alarm or action when theusage is either above an expected range or below an expected range.

Turning now to FIG. 8 a typical cycle of measuring and comparing theexpected power usage signature to actual measurements is shown. Themeasurement data is obtained from the monitored equipment (60) and thegiven expected power usage pattern for the known equipment is loaded(61). Next it can be determined where in the model the currentmeasurements currently are (62). The system then tracks the actual usageand compares it to the expected model determining whether this is withinthe expected range of the model and any allowable tolerance (63).

If there is a variance in the pattern outside of the expected tolerance,the system looks to see if this is repeated over multiple cycles (64).In other words, the system includes software that comprises a filtersuch that an alert or an alarm with not be generated until deviationfrom a threshold occurs for a minimum number of equipment cycles. It iscontemplated that this filter would require at least two completeequipment cycles prior to initiating an alert or an alarm, but one ofskill in the art will understand that more than two cycles may berequired. Additionally, in another configuration, the number of cyclesmay be programmable. Still further, the threshold deviation in terms ofmagnitude of deviation and frequency of deviation may be programmable.If after one cycle the measured power usage pattern resumes a “normal”pattern (65) (e.g., within the tolerance level) no further action istaken; but if the patterns consistently vary from the expected patternbeyond the tolerance level, then an alert is set for action to be taken(66). This action may include remote control of the equipment or aservice call placed to initiate service to the equipment or both.

While the system establishes a baseline curve for equipment beingmonitored, adjustments are necessary to compensate for a variety ofcircumstances. As shown in the figures, the system does not employ afixed curve from which any variance would cause an alert. Rather, abuilt-in hysteresis or range of values sets up a threshold for anyalerts. Further, criteria regarding the capturing of multiple successivecycles exhibiting values outside of these ranges are used to triggeralerts.

As a real-world example, consider a compressor associated with a deepfreezer at a QSR (Quick Serve Restaurant), which has been baselined tocycle On for 10 minutes and Off for 30 minutes. In this situation,imagine the internal freezer temperature having a setpoint of 0 degreesFahrenheit (0° F.). When the temperature rises above 2° F., thethermostat triggers the compressor which cools the freezer to −1° F.This cooling cycle takes 10 minutes in the example being illustrated.Once cooled, the temperature gradually rises as warmer air leaks in fromthe surrounding area causing the temperature to rise to 2° F. Thiswarming cycle takes 30 minutes in the example being illustrated. Thisexample will be used throughout this section to highlight how the systemincorporates variations and compensations.

In this example, the quantity of food and the room temperature will havesome effect on the cycling times. To compensate, a variable of 20% isadded to each of the cycling times allowing for a compressor On-cycle of8-12 minutes and a compressor Off-cycle of 24-36 minutes. Thesethreshold values are established through the monitoring of a largenumber of similar or identical freezer units in operation at similarestablishments and have shown to be accurate cycling times for themajority of business scenarios where doors are opened periodically andfood items are put in and taken out of the unit. Manufacturing andassembly tolerances may also affect how a particular unit may perform.Regardless, over any considerable period, the cycle time will remainwithin this range of expected values and no alerts will be generated.

In the QSR example above, supplies are stocked into the freezer threetimes a week causing the door to remain open for an extended period.This door opening, however, only impacts a single cycle in that the warmair entering the unit while the door is open triggers the cooling cycleof the compressor sooner than the 30 minutes and the compressor remainsoperational for longer than 10 minutes as the temperature rises higherthan 2° F. To illustrate again, consider that the door is open and warmfood is being introduced into the unit. The temperature reading quicklyrises above the 2° F. threshold triggering the compressor to turn on.This event had triggered the cooling cycle before the expected 30 minute+/−20% threshold starting what will be a monitoring of a possibledeviation by the system. If the cooling cycle had just completed, thesystem was only 5 minutes into the normally 30-minute warm up periodwhen the temperature threshold was crossed.

Continuing in the illustration, the staff prop the door open and startplacing more warm food into the freezer. Even as the cooling cycle hasstarted, with the combination of the open door and the relatively warmfood being placed into the freezer, the compressor is prohibited frombeing able to cool the freezer and the temperature continues to rise.The typical 10-minute cooling cycle is extended, and another thresholdis crossed as the system is monitoring a possible deviation. When thedoor is finally closed, we can suppose that the temperature inside theunit is now approximately 6 degrees. Due to the relatively hightemperature in the freezer, it takes the compressor a full 30 minutes tocool the interior to the desired −2 degrees. This deviation in the cycletime has been captured, which exceeds the thresholds in that there was a5-minute warm-up cycle (rather than expected 30 minutes), and there wasa 30-minute cool down cycle (rather than expected 10 minutes). This isclearly a variation outside of the expected or tolerable range (longterm), however, it is also a normal and expected occurrence that willhappen from time to time during normal operation of the business.

Other examples which may affect these cycles intermittently are the useof advanced refrigeration controls that could prohibit the running ofthe compressor during a defrost cycle(s) to reduce peak demand.Additionally, holistic peak demand management systems driven by anintelligent facilities controller may also extend compressor cycles oradjust setpoints when in peak demand scenarios,

To allow for these “normal” deviations, the system may be set with afilter, such that 3 consecutive cycles must exceed the outer bound ofthe expected cycles before triggering an alert. In the previous example,while one cycle included a short 5-minute warm up cycle and a longerthan expected 30-minute cool down cycle, once the interior of thefreezer is cooled sufficiently the freezer will revert to its typicalcycle timing. In such cases, the aberration for a single cycle or twoconsecutive cycles will be ignored and no alert will be generated. It iscontemplated that the filter could be set such that only a certainnumber of total deviations may be allowed during a 24-hour period. Forexample, if the system deviated for two cycles and reverted to “normal”operation, but then deviated for two additional cycles and reverted tonormal operation, this would not cause an alarm even if repeatedindefinitely. The system could have a set number of total cycledeviations allowed during a time period (e.g., max 8 deviations in24-hour period) beyond, which an alert or alarm will occur.

In some installations, additional sensors may also be placed which aremonitored by the system. For example, a door open alert can nullify thecurrent cycle as it is known that there is activity underway that willclearly affect the cooling cycle in progress, These sensors are furtherutilized to generate alerts or alarms if doors are left ajar or openedbeyond preestablished time thresholds.

As another variation in the system, aging equipment is known to degradein efficiency over time. In such cases, energy usage curves have beenmodeled and are applied to the expected threshold values over time. Forexample, even with proper maintenance, the efficiency of a compressorwill drop as will the efficiency of door seals on the freezer. Leakyseals will be seen when the warm-up period is shortened (e.g., insteadof 30 minutes, it is reduced to 26 minutes). Likewise, a less efficientcompressor will take more time to cool the interior of the freezerleading to a lengthened cool down cycle. The curves are not linear andthis age-related compensation may, in one configuration, be accomplishedby adding a skew to the values of the expected range. The system focuseson the ‘lower bound’ of the warming cycle and the upper bound of thecooling cycle as these would typically be the more problematic areas. Ifa known compressor model has been benchmarked or tested and is expectedto degrade by approximately 10% over 5 years in operation, the variationin the monitoring system may be set to increase by 10%. Applied to theabove QSR example, the freezer will now set it's bounds to a 10-minutecool down cycle −20% but +30%. The cool down cycle will be set to 30minutes +20% but −30%. Other aspects of the monitoring could remain thesame or adjusted as desired. For example, it is contemplated that thesystem settings could be programmable.

The system could also be configured to disregard these age-relateddeterioration compensations and generate alerts when the systems exceedsthe thresholds. It will be understood that some operations may decidethat they want to upgrade their equipment more frequently and will wantto know when the equipment degrades to the threshold. Desiring toreplace equipment at an earlier stage could be due to the fact thatmaterials and equipment are increasingly and rapidly becomingsignificantly more efficient. Thus, the realized energy savings of earlyswap out of equipment may be justified. Still others may prefer to getthese alerts and then put proactive maintenance in place, but it isexpected that such maintenance would rather keep the units from fallingbelow the thresholds established by the system. These settings areconfigurable/programmable to allow for a multitude of indications. Whilefalse positive alerts are not overly problematic, they can be expensiveif they trigger unwarranted service calls or cause automaticmodification in the operation of the equipment.

Business-related factors may also affect the cycle times beingcontemplated. For example, a QSR doing twice as much business may openthe freezer door twice as often. Door opening alarm systems and themulti-cycle requirement for alerts can be used to adjust for thesevariances when it comes to freezers, but other QSR equipment such as icemakers, bread ovens, and HVAC may all have to work additional cycles toservice more patrons.

Other factors such as compressor placement may also affect efficiencyand introduce seasonality variances with temperature. Such factors arecompensated for in a similar way as the aging of equipment. For example,outside temperature can be factored in to compensate for compressorhaving to work harder in warmer seasons. For example, a larger offsetcan be used to factor in a compensation variable allowing for longercooling times during these times.

While the example used to illustrate the compensation was a freezercompressor, many other types of systems, such as a HVAC system, wouldhave direct impacts from temperature variations that are built into thecompensation model directly. For example, the temperature variation ofoutside temperature versus a desired inside temperature will have adirect bearing on the expected cycle times and must be accounted for.The system could use these dynamically changing temperaturedifferentials to determine the cycle times, while the deviationthresholds could remain consistent.

For example, in the case of an HVAC system, cooling and warming cyclesmay be much closer in timing as the spaces being cooled or heated aremore temperature dynamic. In one example, a 5-minute cooling cycle and a5-minute warming cycle could be an expected normal where factoring atemperature variation could be as follows: The cooling time (when usingAir Conditioning in Summer) would be increased as the temperaturevariation increased due to warmer air being mixed into the space duringthe cooling cycle. The compressor must also work harder in highertemperatures if placed outside. Further, the warming cycle would bereduced, as the large temperature differential blends warmer airincreasing the temperature faster.

So while the 5-minute cool (HVAC On) and 5-minute warm (HVAC Off) cyclemay hold for keeping the room at 68° F. when the outside temperature is80° F., when the outside temperature reaches 100° F., the cycle couldadjust to 7-minute cool (HVAC On) and 3-minute warm (HVAC Off). The 20%variance will remain regardless of the calculated cycle times beforetriggering alerts; however, these cycle times are calculated from thedifferential. This delta of the outside temperature versus the desiredindoor temperature will directly impact the cycle times. Cooltime=outside/inside temp*cool time*X. Warm Time=Inside Temp/Outsidetemp*cool time*X. Where X is determined based on other externalvariables such as building materials, insulation, outside facing wallsetc. These values are baselined and fine-tuned by the system as itlearns the environment of a particular structure.

Turning to FIG. 9 block diagram is shown of a typical installation ofthe system for a particular piece of equipment, in this case, arefrigerator (70) with a compressor (71). A power consumption meter (75)is installed, which may be on the power plug, at the power panel orbuild into the refrigerator. This meter (75) sends data to a localcollection device (74), which may comprise a facilities controller, alocal computer or an internal storage device with communicationscapabilities built into the meter (75) itself. Data is sent from thecollection device (74) to a computer system (76), which may be remotelylocated (e.g., in the cloud), or on the premises where the data is thenstored and correlated and matched with patterns.

In addition to the power usage data of the refrigerator (70) beingcaptured by the meter (75) and transmitted to the computer (76) througha collection device (74), other sensors, such as, a room temperature andhumidity sensor (27) and a door open sensor (73), may be deployed tocapture events that could impact adherence to the model. These varioussensors (72)(73) send data to a collection device (74), which in turn,sends the data to computer (76). Each event is time stamped so that thecorrelation of measurement data from all sensors (7 2,72,75) can becombined.

Types of Problems that can be Discovered. A discussion will now bepresented with respect to the measurement and analysis of what kinds ofproblems can be detected and how these are reacted to by the system aswell as how the models are established and vary over time. Theseexamples are provided to illustrate how the system can adapt and learnto adjust for variances over time and variances due to external factors.The system will be able to learn variances that can affect the expectedpower usage pattern or model. Additionally, as more data is gatheredabout a particular piece of equipment installed at a particularlocation, the expected power usage pattern or model will evolve throughmachine learning and adapt to become more accurate and able to predictproblems more quickly.

When deploying devices of a certain type, one can learn from thebehaviors of similar devices and build a model from these as a startingpoint. Ideally, one would have established a model for each piece ofequipment using actual measured data that can be used to form a baselinefor future installations of such equipment. If no such model exists, thefirst installation can be used to establish the expected pattern ormodel, and then subsequent device installations can utilize the firstmodel where the expected power usage pattern established for thatparticular piece of equipment can be adjusted based on the variancesseen assuming the variances are not too great to indicate amalfunctioning piece of newly installed equipment.

Similarly, if a model has been in use or data has been collected over afull or partial lifecycle of a piece of equipment, the expected powerusage model may be modified to account for age and normal degradation ofthe equipment. This age-modified model can then be used to measureagainst other similar pieces of equipment installed and monitored by thesystem. As an example, Maytag model 1 refrigerators are installed andmonitored over a 10-year period. It is observed that after 9 years, somemodels are showing degradation that is detected by measuring the powerusage. Based on this information, it can be expected that similardegradation will be seen in similar equipment that reach the same pointin the lifespan of the particular equipment.

The ability to capture and understand normal power usage cycles andadjust the monitoring to avoid false positives will provide valuableinformation to companies looking to tightly control energy costs andobtain the most use out of installed equipment without having to dealwith the potential negative consequences of using the equipment tillcatastrophic failure. Additionally, it should be noted that if it isobserved that a particular location with particular equipment has thedoor of a refrigerator open much more often or for substantially longerperiods of time than other locations performing similar functions, somebenchmarking and comparison can also be accomplished to see wherebusiness operations can potentially be improved. While the equipment maybe functioning optimally, there may be an opportunity to adjustoperating procedures to lower operating costs.

Furthermore, knowing and understanding cycling duration of particularequipment and correlating this with the cost of power usage would allowone to predict and show business owners potential savings throughutilization of the currently discussed system.

The following are some parameters that would be advantageous to monitor.These could provide data to adjust the expected power usage pattern orsignature, or they could be independent measurements that could beindependently monitored so as to lower operating and maintenance costs.These parameters are only provided to be illustrative to allow for abetter understanding of some types of data that would be advantageous togather for processing by the system and are not intended to be limiting.

Vibration Measurement. Symptom: Sensing vibration or shuddering. ActionTaken: Reduce speed of Variable Speed Motor: Dispatch service technicianto inspect for mechanical wear, loose fasteners, drive belt condition,

High Frequency Sound Measurement. Symptom: Sensing high pitched sound.Process: Correlate with whether compressor has to run longer to reachset point or whether there is increased power usage for the compressorunit, or higher temperature of the compressor unit, Action: Dispatchtechnician to check refrigerant level, high side head pressure.

Combined Vibration and Sound Measurement, Symptom: sensing high pitchedsound and vibration. Process: Correlate with whether equipment is usingan increased amount of power to run. Action: dispatch technician forpossible belt or bearing replacement.

Sound Measurement. Symptom: ticking sounds emanating from the equipment.Process: Correlate with ice sensor or temperature inside of freezer.Action: initiate defrost cycle to determine if fan is icing up.

Lower Instantaneous Power Consumption. Symptom: Consuming lesselectricity at any one point in time. Process: Correlate with insidetemperature refrigeration unit. Action: If temperature inside ofrefrigeration unit is too high, failing or inoperative compressor.

Increased Duration of Power Consumption Cycles, Symptom: Equipmentstaying on longer than expected. Process: Correlate with low pitchedknocking sound from compressor sensor or high pitched sound (e.g., takeslonger to cool—but once cool stays cool). Action: suspect lowrefrigerant level or faulty compressor.

Increased Frequency of Power Consumption Cycles. Symptom: Insidetemperature of refrigeration unit rising too quickly. Process: Correlatewith other sounds. Action: suspect leaky door gasket, open door, orother mechanical failure.

It should be noted that sensors can initially measure all these datapoints (e.g., sound measurements, vibration measurements, etc.) togenerate expected baselines for those measurements. These can then bematched or compared to corresponding measured factors to determine ifthese measured indicators are within allowed tolerances. For example,when a piece of equipment is first installed, the noise produced by theequipment may be very low and vibration may be minimal. However, as theequipment ages or if it becomes damaged for some reason, changes tothese factors could be detected and actions, alerts or even alarms canbe generated by the system as needed. The collected data could be usedby the system to generate specific actions, alerts or alarms including,for example, the Alarm Name, High Air Temp, Low Air Temp, ExcessDefrosts, Super Heat Temp and so on along with a host of differentactions to modify the functioning of the equipment.

Referring to FIG. 10 a block diagram of the system is illustratedincluding a piece of equipment 100 having a control input 102. Theequipment 100 can comprise virtually any type of equipment that iscontrolled including, for example but not limited to, HVAC equipment,refrigeration equipment, freezer equipment, ovens and baking equipment,industrial equipment, manufacturing equipment and the like. Likewise,the control input 102 can be any type of single device or multiplecontrol devices or processed inputs whether locally or remotely locatedfrom the equipment 100.

Also shown in FIG. 10 is computer 200, which could comprise any type ofgeneral purpose or special purpose computer capable of receiving andprocessing data. A storage 202 is accessible by computer 200 and isprovided to store data.

Computer 200 is coupled to current sensor 204, which in one embodimentcould comprise current transformers for measuring current supplied fromelectrical power source 104 to equipment 100. The current sensor 204 cancomprise any type of current transformers and may measure single phaseor three phase current to generate current data that is received bycomputer 200 and saved in storage 202 with a time stamp. It should benoted that while sensor 204 is labeled as “current” sensor, it iscontemplated that sensor 204 could be provided with the ability tomeasure voltage such that both current and voltage could beinstantaneously measured and power usage can be easily determined.

Also depicted in FIG. 10 are sensor 206 and sensor 208. These couldcomprise virtually any type of sensors providing secondary data such as,environmental temperature in the vicinity of the equipment, exterior airtemperature outside the facility, exterior humidity or room occupancy.It is conceived that these could be “dynamic” measurements in that theycould be made and transmitted in real-time or close to real time. Inthis way environmental measurements that can have a real effect on therun time of equipment can be taken into account so that an alarm doesnot sounds due to the equipment running for a longer (or shorter) periodof time based on environmental factors. Alternatively, the sensors couldbe measuring parameters more directly associated with the equipment thatis being measured including, for example, a door sensor, or a vibrationdetector or the like.

It should be noted that while the computer 100 is shown coupled toequipment 100 and sensor 204, 206, 208, it will be apparent to those ofskill in the art that these connections could be hard-wired or theycould be wireless connections. Likewise, these various devices could becoupled to each other via a network connection providing maximumflexibility.

FIG. 11 depicts a flow and decision chart for the initial setting up ofequipment by the system. At step 300 the equipment is initiated. Fromthere the system then measures the energy or power usage 302 of theequipment during a calibration time period, which is saved in a storageas an actual power usage signature or pattern. It will be understood bythose of skill in the art that the calibration time period may beselected to encompass multiple repeating cycles of operation for theequipment. The sensor may in one configuration measure current, and inanother configuration may also measure voltage. However, it iscontemplated that the sensor may comprise a device that measure anamount of natural gas or oil consumed by the equipment.

The measured energy or power usage signature is then compared to anexpected power usage signature 304. The expected power usage signaturecould be data received from the factory relating to the expected powerconsumption characteristics of the equipment. Alternatively, it couldcomprise a pattern or signature generated by the system based on datainput provided by a user. Still further, the expected power usage 304signature could comprise historical data gathered by the system for anidentical piece(s) of equipment monitored in that or another location.In short, the system will compare the measured power usage signaturewith the expected power usage signature to determine if the actual usagesignature exceeds a threshold deviation 306. It is conceived that thereare a number of ways in which this can be done, however on way is tomatch data points between the expected power usage signature and themeasured power usage signature. If the plotted points do not exceed athreshold deviation, then the system can proceed with continuouslymeasuring the power usage of the equipment, which will be compared to anexpected signature for analysis. It is contemplated that the initialexpected power usage signature could be modified by the initial measuredpower usage signature. For example, the system could go through a numberof cycles which may span some time, even over a few days to create astable running cycle measurement. That data could then be used as theexpected power usage signature, or that data could be used to modify theoriginal expected power usage signature. Likewise, while not shown inFIG. 11, gathered historical data could be used to modify the expectedpower usage signature so that the expected power usage signatureaccounts for expected degradation of the equipment over time.

Referring back to step 306, if the plotted points deviate too far fromthe expected power usage, then an alert can be generated 308. Thisinitial alert indicates that something is wrong with the newly installedequipment or newly monitored equipment. The system then has the optionto adjust a setting of the equipment and/or run a diagnostic 310. Theadjustment could be to slow the equipment to a set percentage ofoperation. Additionally, the adjustment could be to run the equipmentthrough a full range of diagnostic functions which would allow for thegathering of data as the equipment is operated in various differentways. It is contemplated that in one configuration additional sensorscould be used to gather the diagnostic data, which in turn, would betransmitted to a computer for analysis.

After adjusting an operating parameter or running a diagnostic, thesystem could then measure the operating parameter(s) of the equipment312. This step could include re-measuring the current the equipment isusing or determining that the diagnostic results are sound and noproblem exists. If it is determined that no problem exists, the systemcould then return to the step of measuring the power usage 302 and beginthe initial measurement process again.

If, however, it is determined that the equipment is not functionallywithin normal operating parameters, the system could then turn theequipment off 314. This step could also include the sending of an alarmor a notification of equipment malfunction that could includetransmission of the diagnostic data as a report accompanying the alarmnotification.

It is further understood that supplemental sensors can provideadditional input data relating to the functioning of the equipment. Forexample, a sensor may indicate that a window in a space served by theequipment is open and/or a temperature sensor in the space may furtherindicate the temperature is rising. Additionally, a door sensor mayindicate that a refrigerator or freezer door is open or ajar and/or atemperature sensor in the refrigerator or freezer may further indicatethe temperature is rising despite the operation of the equipment.

FIG. 12 is another flow and decision chart according to one aspect ofthe invention. At step 400, the system sets the expected power usagesignature and threshold. This may occur as previously described inconnection with FIG. 11. Alternatively, this could be automaticallyretrieved by the system based on an identification of the piece ofequipment that is being monitored. This identification could be due todata input by a user, or it could be by matching of a measured powerusage signature with known power usage signatures. Likewise, thethreshold deviation could comprise a manually entered or programmeddeviation (e.g., ±3% or some other figure), or it could be data providedfrom the factory or even a range that is generated by the system basedon historical data of similar equipment.

Once the expected power usage signature and thresholds are set, thesystem will then dynamically measure environmental or system factors402. This could include, for example, accounting for the outside airtemperature or for whether a door sensor indicates that a door to arefrigerator is open and the duration of the event. In the firstinstance, if the equipment being monitored comprises a compressorassociated with HVAC equipment, it will be expected that the equipmentwill have to work longer and more frequently during the hot summermonths. The delta (Δ) between the inside air temperature and the outsideair temperature will be a factor in adjusting the threshold to notindicate an alert or an alarm based on the fact that the compressor hasto work harder to maintain a temperature set point during hot summermonths than it does during cool spring or fall months. Likewise, when adoor to a walk-in refrigerator or freezer is opened, it can be expectedthat the temperature inside the refrigerator or freezer will rise andthe temperature rise will be commensurate with the duration of the event(e.g., the length of time the door is open). This can be factored in tothe threshold value, or it could be that the system in the example ofthe door open event, will simply discard the cycle data as it is asingle non-repeated event unlike the repeated event of the compressorrunning during hot summer months.

Once the system has measured environmental or system factors 402 anddynamically (in real time or close to real time) adjusted the thresholdbased on the environmental or system factors 404, the system willproceed with active power usage measurement 406. The measured actualpower usage is then compared to the expected power usage signature 408and a determination is made if the measured usage exceeds the thresholddeviation value 410. If the measure power usage does not exceed thethreshold value, the system continues to monitor the actual power usage406.

If, however, it is determined that the measured power usage does exceedthe threshold deviation, the system will move to generate and alert 412.It will be understood by those of skill in the art that the alert may begenerated when the actual power usage signature indicates that either ahigher than expected energy usage is occurring or that that a lower thanexpected energy usage is occurring. In either situation, the actualenergy usage for the equipment exceeds the threshold deviation (above orbelow the curve) to such an extent that the system determines a problemhas developed with the equipment that requires further analysis.

Generation of an alert may comprise a visual indication, an audioindication, a digital message or any combination or the like. If analert is generated, it is contemplated that the system could then adjustthe operation of the equipment or run a diagnostic 414. The adjustmentor diagnostic could comprise any type of event as previously described.It is further contemplated that the alert could comprise a text or emailmessage to maintenance personnel that the system needs to be checked.Likewise, the alert could be the automatic transmission to servicepersonnel to make a service call to check the equipment. In oneconfiguration, diagnostic data could be transmitted with the text oremail or a link that could be clicked on by the maintenance or servicepersonnel could be provided allowing the individuals to access thesystem data collected and associated with the equipment in question.This could allow personnel to remotely determine what the currentsituation is to prioritize service calls and potentially indicating whatparts may be needed to be brought to the call if the equipment ismalfunctioning.

Alternatively, the adjustment of the equipment could comprise turningthe equipment off and re-initializing. Once the system has made theadjustment to the equipment and/or performed a diagnostic, the systemthen determines if the current measured operation of the system isnormal 416. If so, the system then returns to measuring theenvironmental and system factors and continues the monitoring aspreviously described to monitor the equipment. If, however, the systemdoes not see the equipment returning to normal expected function, thesystem may then, depending on the severity of the measured values, turnthe equipment off 418 and generate and alarm condition or notification420. The alarm condition could be indicated by visual, audio or digitalmessaging to a user or group of users or service personnel. As in thecase with the generation of the alert 412, it will be understood bythose of skill in the art that the alarm may be generated 420 when theactual power usage signature indicates that either a higher thanexpected energy usage is occurring or that that a lower than expectedenergy usage is occurring at step 416.

While the invention is susceptible to various modifications, andalternative forms, specific examples thereof have been shown in thedrawings and are herein described in detail. Is should be understoodhowever that the invention is not to be limited to the particular formsor methods or embodiments disclosed.

What is claimed is:
 1. A control system automatically learning andadapting to the energy usage of a piece of equipment, the systemcomprising: a computer having a storage and coupled to a network; afirst sensor coupled to the network and measuring an energy usage of thepiece of equipment; a second sensor coupled to the network and measuringa parameter selected the group consisting of: temperature, humidity,wind, a door status, an occupancy status and combinations thereof; saidstorage having saved thereon: expected energy usage data comprising anexpected energy usage the piece of equipment is expected to followduring operation; deviation data comprising a threshold deviation fromthe expected energy usage data; said first sensor generating energyusage data corresponding to the measured energy usage of the piece ofequipment; said second sensor generating second sensor datacorresponding to the measured parameter; software executing on saidcomputer receiving the second sensor data and modifying the thresholddeviation based on the received second sensor data; said softwarereceiving the energy usage data and comparing the energy usage data tothe expected energy usage data; wherein when the energy usage dataexceeds the modified threshold deviation, said software initiates anaction selected from the group consisting of: running the equipmentthrough a diagnostic routine, setting the equipment to a preset level ofoperation, setting the equipment to a preset duration of operation,turning the equipment off, cycling the equipment, generating an alarmand combinations thereof.
 2. The control system according to claim 1,wherein said first sensor comprises a current sensor.
 3. The controlsystem according to claim 2, wherein the current sensor comprises acurrent transformer.
 4. The control system according to claim 1, whereinthe expected energy usage data is based on criteria selected from thegroup consisting of: a time of day, a date, a geographic location wherethe piece of equipment is installed, a perm rating of a building inwhich the piece of equipment is installed, historical usage data for thepiece of equipment, an expected degradation in the piece of equipmentefficiency, and combinations thereof.
 5. The control system according toclaim 1, wherein said software comprises a filter such that the alarmwith not be generated until the modified threshold deviation is exceededfor a minimum number of equipment cycles.
 6. The control systemaccording to claim 5, wherein the minimum number of cycles is at leasttwo cycles.
 7. The control system according to claim 5, wherein theminimum number of cycles is programmable.
 8. The control systemaccording to claim 1, wherein the expected energy usage data isreflective of cycling of the piece of equipment including: a frequencyin the cycling of the equipment, a duration of each cycle, a magnitudeof energy usage during each cycle, and combinations thereof.
 9. Thecontrol system according to claim 1, wherein the modified thresholddeviation includes an upper range of energy usage and a lower range ofenergy usage.
 10. The control system according to claim 9, wherein thealarm is generated when the energy usage data exceeds the upper range ofenergy usage or the lower range of energy usage and combinationsthereof.
 11. A method for automatically learning and adapting to theenergy usage of a piece of equipment with a computer having a storageand coupled to a network, the method comprising the steps of: measuringan energy usage of the piece of equipment with a first sensor coupled tothe network; measuring a parameter with a second sensor coupled to thenetwork, the parameter selected the group consisting of: temperature,humidity, wind, a door status, an occupancy status and combinationsthereof; generating energy usage data with the first sensorcorresponding to the measured energy usage of the piece of equipment andtransmitting the energy usage data to the computer; generating secondsensor data with the second sensor corresponding to the measuredparameter and transmitting the second sensor data to the computer; thecomputer accessing deviation data on the storage, the deviation dataincluding a threshold deviation from expected energy usage data; thecomputer modifying the deviation based on the received second sensordata to generate a modified threshold deviation; the computer comparingthe energy usage data to the expected energy usage data to determine ifthe energy usage data exceeds the modified threshold deviation; whereinwhen the energy usage data exceeds the modified threshold deviation, thecomputer initiates an action selected from the group consisting of:running the equipment through a diagnostic routine, setting theequipment to a preset level of operation, setting the equipment to apreset duration of operation, turning the equipment off, cycling theequipment, generating an alarm and combinations thereof.
 12. The methodaccording to claim 11, wherein the first sensor is a current sensor. 13.The method according to claim 11, wherein the expected energy usage datais based on criteria selected from the group consisting of: a time ofday, a date, a geographic location where the piece of equipment isinstalled, a perm rating of a building in which the piece of equipmentis installed, historical usage data for the piece of equipment, anexpected degradation in the piece of equipment efficiency, andcombinations thereof.
 14. The method according to claim 11, wherein whenan alarm is generated by the computer, the alarm is only generated whenthe energy usage data a exceeds the modified threshold deviation for aminimum number of cycles for the piece of equipment.
 15. The methodaccording to claim 14, wherein the minimum number of cycles is at leasttwo cycles.
 16. The method according to claim 14, wherein the minimumnumber of cycles is programmable.
 17. The method according to claim 11,wherein the expected energy usage data is reflective of cycling of thepiece of equipment including: a frequency in the cycling of theequipment, a duration of each cycle, a magnitude of energy usage duringeach cycle, and combinations thereof.
 18. The method according to claim11, wherein the modified threshold deviation includes an upper range ofenergy usage and a lower range of energy usage.
 19. The method accordingto claim 18, wherein the alarm is generated when the energy usage dataexceeds the upper range of energy usage or the lower range of energyusage and combinations thereof.